Explore the cutting-edge tools revolutionizing the industry. 

Computer-aided design (CAD) is already an essential tool for most biopharmaceutical research teams. But as CAD tools such as deep learning (DL), machine learning (ML), and artificial intelligence (AI) evolve faster than we can imagine, even the most tech-savvy teams risk missing out on the transformational benefits of these state-of-the-art tools. Explore the nearly unimaginable advantages of the latest CAD advancements and learn how to break down barriers to unlocking the full potential of these innovations, even if your organization has yet to reach for the key.

The Capabilities of Cutting-Edge CAD

Experts in AI, ML, and DL have documented a few primary areas of digital transformation within the biopharmaceutical realm.

Drug discovery

ML gives labs the ability to quickly process nearly infinite drug datasets to find matches that could fuel the discovery of new pharmaceutical treatments. 

As reported by Genetic Engineering & Biotechnology News, the firm Atomwise pioneered the use of convolutional neural networking—a form of ML used in common consumer tech like social media photo tagging—for more than 550 initiatives, including drug discovery, dose optimization, and toxicity screening.

Personalized medicine

This term describes the use of a type of ML called predictive analytics, in which a patient’s individual health data provides the physician with detailed information about their genetics, risks, and possible diagnoses. The partnership between IBM Watson Oncology and Memorial Sloan Kettering exemplifies the area of personalized medicine as they drive research into the use of this modality to optimize patient treatments and outcomes. The availability of mobile apps, devices, and biosensors for remote health analysis will dramatically expand this area in the coming years.

Patient and site selection for clinical trials

Biopharma companies can significantly reduce the cost and time investment of clinical trials with the application of ML algorithms. In a 2018 article, research firm Evidera cited an analysis of 151 international clinical trials at nearly 16,000 sites. The study, which appeared in the journal Therapeutic Innovation and Regulatory Science, uncovered the difficulty of finding appropriate patients for clinical trials, especially for central nervous system conditions. The use of AI models can potentially use data mining to find subjects that have not yet been diagnosed with the disease in question.

Overcoming barriers to focus on the future

Research institutions of all sizes struggle to adopt emerging tech. The most common blockades to biopharma progress in this area include:

• Internal culture that resists innovation
• Limited infrastructure and resources to invest in technology
• Misplaced commitment to legacy tools and practices that prevent experimentation
• Lack of digital leadership from C-level executives
• Limited access to clean, reliable datasets
• Barriers to interoperability among collaborators, often even within the same organization
• Concerns about data privacy and security, as well as other regulatory issues

The ethical implications of artificial intelligence and machine learning also pose issues, as technology evolves faster than we can answer questions about bias, transparency, and related concerns.

Today’s emerging biopharma tech will rapidly evolve and replace itself with tomorrow’s innovations. Within just a few years, modern labs that realize the possibilities of AI, DL, and ML will leave traditional biopharma firms in their dust with little hope of recovery. For example, companies that do not adopt advanced AI methods to recruit participants for clinical trials will struggle to complete the necessary research to produce new products. Deloitte estimates that fewer than 17 percent of the 30,000 trials registered on ClinicalTrials.gov in 2018 ever published results. 

In a 2019 study by Deloitte Insights, 79 percent of biotech respondents said their companies planned to implement new CAD advancement within the next five years, with 58 percent citing digital transformation as a top leadership priority. The prior year, a benchmarking study by the research firm found that 60 percent of biotech firms used machine learning, and 96 percent anticipated using it in coming years.

RCH Solutions is a global provider of computational science expertise, helping Life Sciences and Healthcare firms of all sizes clear the path to discovery for nearly 30 years. If you’re interesting in learning how RCH can support your goals, get in touch with us here.